{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "drilling_optimzn.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "9XWyI5oKlU-3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from scipy import stats\n",
        "df = pd.read_excel('Torque.xlsx',sheet_name='Tork Dt')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wCDF6tGQmebc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "e3480c07-c524-46d0-ce69-07b873222e14"
      },
      "source": [
        "df.head(3)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Torue Feet-lbs</th>\n",
              "      <th>MD Feet</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>96.153846</td>\n",
              "      <td>12225.895317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>288.461538</td>\n",
              "      <td>12225.895317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>480.769231</td>\n",
              "      <td>12225.895317</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Torue Feet-lbs       MD Feet\n",
              "0       96.153846  12225.895317\n",
              "1      288.461538  12225.895317\n",
              "2      480.769231  12225.895317"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "myG6D7m2mqGk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "01c0ccd8-8d41-4e48-e5cf-90c19a0f284a"
      },
      "source": [
        "plt.scatter(x='MD Feet',y='Torue Feet-lbs',data=df)\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fb9d424ccf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2SyNSMBApwHhHN4OCgzQGBQORIpRShZRmpNZdUOOz1BN1LRUpQrwrxs0XzR9X99S06AI8mkFV6p2CgUgW0RlTSwkKaUOJpMYtSN1SMBDJY+w02gZ0TGml9ZRiJtNOSSSH2bT7QPkzKVIitRmIjFMpPZEM1MgsNaEGZJEKi/ZESjce59PW2sK//1yMJ18a5M2hhAKEVJyCgUgVFTMNxtjA0dbaws0XzVdAkIpQMBCpgWhVUrF/aeqSKpWgYCBSYws3PlH0YDYNZJNy0zgDkRpbvXgOba0to9Ly9UcaO6W2xitIpSgYiFRJeiBbuntqrL2NyxecfVKAyEfjFaQSVE0kUmPjnQ/pFINPfKxVVUhSFFUTidSpeFeMp9acz+blnUWVEkbCmgxalU3KQcFApE6MrUYqdpRzIjnMqp4+Fm58QkFBijap1hkQkY/Eu2KjqnrGs2xnupSQfj2RQqjNQKQB7Nw3wHXbn2W4iL/XFjNG3NWeIKNonIFIgytljQWNV5A0BQORCWDs5HgeGpGLpYV3mte4g4GZ3Ql8BXjH3f9tSJsK9AAzgdeAS9z9mJkZ8JfAhcBx4Gvu/stwzgrghvCyG9x9S0j/HPBToA14GPi2FxChFAxEUkpdlU2lhuZSStfSnwJLxqStAR5399nA4+E5wAXA7PBYCdwe3nwqcBPweeA84CYz6wjn3A58M3Le2PcSkRzG9kJqseLWWRg7ylldVJtT3mDg7v8IHB2TvAzYEra3APFI+l2esgdoN7PpwGLgMXc/6u7HgMeAJWHfJ9x9TygN3BV5LREpUHqswqsb/5hbLjmn6FHNUYnkMNdtf1YBocmMd5zBGe5+JGy/BZwRtmPAG5HjDoe0XOmHM6RnZGYrzazXzHoHBwfHmXWRia3U8QoAw+5c29PHzDUPadxCkyh5nIG7u5lVpRXa3e8A7oBUm0E13lOkEWUar1DswjvpY9IT5K3btZ91S+epPWGCGm8weNvMprv7kVDV805IHwDOihw3I6QNAF8ak/4PIX1GhuNFpIyiwWG8y3UOJZJc29PHqp4+9UKagMZbTbQLWBG2VwD3R9KvtJQFwLuhOmk3sMjMOkLD8SJgd9j3npktCD2Rroy8lohUQLR9Yd+Ni9j0H84puNE5Wlq4tqePG3b2Vy6jUlV5SwZm9jNS/9WfbmaHSfUK2ghsN7OrgNeBS8LhD5PqVnqQVNfSrwO4+1Ez+zPgmXDc99w93Sj9LT7qWvpIeIhIlaT/uy+2e6oDW/ccYuueQ3SEMQ/qntq4NOhMRIDxtStko3Wc61e2cQaaqE5EgJPbFYqdIC8q3T01/bpS/zSFtYicJN4VY9+Ni9i8vJNYe9u4XmPYXQPYGoiqiUSkIDfs7GfbnkPjqj5S76P6oYnqRKRkpbQraHK8+qA2AxEpWa7xCkPHkzmDQ7RbqhbfqT8qGYhIWYxn9lSVEqpPJQMRqaj0F3q6GqkQKiXUD5UMRKTsVEqoX2pAFpGqGk9jsxqZK0/BQERqJhoYCqUV2CpDwUBEaq7UJTrb21o1jXaJSln2UkSkLKIL74zHUCLJqp4+Otc/qpHNZaZgICJVlZ5Ce/PyznEvz5leW0FTaJePupaKSE2M7Ypa7Ijm9BTaDz57hKFEkhYzht3V+DxOCgYiUjPlWoENUhPjQWrswup7NGNqsdSALCJ1qdRptEENzpmoN5GINKRyBAVQYEhTbyIRaUjptRWuWHA2ha3UnFm6J9JnvvuIeiJloGAgIg1hQ3w+t4bFdgzomNJK6ynFh4dEckTdUzNQNZGINCxVIRVPbQYiMmGVKyjAxA8MCgYiMuHt3DfAd+59jvc/HCn5tSZqUFADsohMePGuGAc2XMAVC85mHM0JozTbKGeVDERkQlO7wmha6UxEmtLYUc7jDQxDiSSrfz5xRzarmkhEmkZ6zMLm5Z2cNrn4SfKSI85125+dkF1SVU0kIk2rlJJCo1YbqTeRiEgeN+zsZ9ueQwXPnmrA5QvOZkN8fiWzVVbqTSQikkd6lHPHlNaCjndg255DE6LaSMFARCQi2q5QSFBwYP0D+yufsQpTMBARySAaFPKtyHbseLLhxyOoa6mISA7pBuJ8Dc1b9xxi655DDbvSWkklAzN7zcz6zazPzHpD2lQze8zMXg4/O0K6mdltZnbQzJ4zs3Mjr7MiHP+yma0o7ZJERMorOo12PgNDCdbu6G+4doRyVBN92d07I63Ta4DH3X028Hh4DnABMDs8VgK3Qyp4ADcBnwfOA25KBxARkXqyIT6f9rb87QiJ5DCbdh+oQo7KpxJtBsuALWF7CxCPpN/lKXuAdjObDiwGHnP3o+5+DHgMWFKBfImIlGzd0nkFLbIzMJRg4cYnGqaEUGowcOBRM9trZitD2hnufiRsvwWcEbZjwBuRcw+HtGzpJzGzlWbWa2a9g4ODJWZdRKR48a4Ylxe46trAUKJhJrsrNRh80d3PJVUFdLWZ/UF0p6dGtJVtVJu73+Hu3e7ePW3atHK9rIhIUaKrrgE5A0OjjEUoqTeRuw+En++Y2X2k6vzfNrPp7n4kVAO9Ew4fAM6KnD4jpA0AXxqT/g+l5EtEpNLGToC3afcBBoYSGY914Lrt9T3J3bhLBmZ2mpn9TnobWAQ8D+wC0j2CVgD3h+1dwJWhV9EC4N1QnbQbWGRmHaHheFFIExFpCPGuGE+tOf9ESSGTYXdW9fTxme8+UpelhFKqic4A/q+ZPQv8AnjI3f8e2Aj8kZm9DPxheA7wMPAKcBD4MfAtAHc/CvwZ8Ex4fC+kiYg0lNWL5+RtS0gkR1hVh+0ImqhORKSMipns7ooaTHKniepERKog3bjcYvn7G23dc6huSggKBiIiZRbvinHLJecU1P10655DdK5/tObtCAoGIiIVkB6PUIihRLLm4xEUDEREKmRDfH7BS2w6ta02UjAQEamgeFeM/d9bUtAkd1C7gKAprEVEqiDda6iQnkZb9xwadU41qGQgIlIlxSyrWe0SgoKBiEgVFbM2QjXnNFIwEBGpgQ3x+XkDggPXbu+rSkBQMBARqZGCAoLD6p8/W/GAoGAgIlJDhQSE5Iiz/oH9Fc2HgoGISI0VEhCOHU9WtEFZwUBEpA4Usr5yJRuUFQxEROrEuqXzaG3JPqORQ8WqixQMRETqRLwrxqaLzyHXhKeVqi5SMBARqSPxrhi3XtKZc8bTrRWoLlIwEBGpM4XMeHr9feUtHSgYiIjUoXwNyr/9YLispQMFAxGROrVu6byc+zftPlC291IwEBGpU/GuGG2t2b+m3xxKlO29FAxEROrYzRd9Nuu+9gJmPy2UgoGISB3LVTrwfAsjFEHBQESkzv1rciRj+ruJZNneQ8FARKTOZasOUjWRiEgTyVYdpGoiEZEmkq06SNVEIiJNRNVEIiKiaiIREVE1kYiIoGoiEREB3k8OF5U+HgoGIiJ17niWQWfZ0sejboKBmS0xswNmdtDM1tQ6PyIizaQugoGZtQA/BC4A5gKXmdnc2uZKRKQ+ZFv1LNdqaMWqi2AAnAccdPdX3P0D4G5gWY3zJCJSF7L1IC1jz9K6CQYx4I3I88MhbRQzW2lmvWbWOzg4WLXMiYjUUqy9raj08aiXYFAQd7/D3bvdvXvatGm1zo6ISFWsXjyHttaWUWltrS2sXjynbO8xqWyvVJoB4KzI8xkhTUSk6cW7UhUlm3Yf4M2hBGe2t7F68ZwT6eVQL8HgGWC2mc0iFQQuBf5jbbMkIlI/4l2xsn75j1UXwcDdPzSza4DdQAtwp7vvr3G2RESaRl0EAwB3fxh4uNb5EBFpRg3VgCwiIpWhYCAiIgoGIiIC5uVcHaGKzGwQeH2cp58O/LqM2amViXAdE+EaQNdRb3Qd2f2eu580UKthg0EpzKzX3btrnY9STYTrmAjXALqOeqPrKJ6qiURERMFARESaNxjcUesMlMlEuI6JcA2g66g3uo4iNWWbgYiIjNasJQMREYlQMBARkeYKBvW+zrKZnWVmT5rZC2a238y+HdKnmtljZvZy+NkR0s3MbgvX85yZnRt5rRXh+JfNbEUNrqXFzPaZ2YPh+SwzezrktcfMJof0U8Pzg2H/zMhrrA3pB8xscQ2uod3M7jGzl8zsRTP7QoPei2vD79PzZvYzM/tYI9wPM7vTzN4xs+cjaWX7/M3sc2bWH865zczKuYpkvuvYFH6vnjOz+8ysPbIv4+ec7fsr270smrs3xYPUbKi/Aj4FTAaeBebWOl9j8jgdODds/w7wz6TWhP5zYE1IXwN8P2xfCDxCainUBcDTIX0q8Er42RG2O6p8LX8C/B3wYHi+Hbg0bP8I+K9h+1vAj8L2pUBP2J4b7tGpwKxw71qqfA1bgP8cticD7Y12L0itGPgq0Ba5D19rhPsB/AFwLvB8JK1snz/wi3CshXMvqOJ1LAImhe3vR64j4+dMju+vbPey6HxW65ey1g/gC8DuyPO1wNpa5ytPnu8H/gg4AEwPadOBA2H7b4DLIscfCPsvA/4mkj7quCrkewbwOHA+8GD4Y/t15Jf/xL0gNW35F8L2pHCcjb0/0eOqdA2/S+pL1MakN9q9SC8pOzV8vg8CixvlfgAzx3yJluXzD/teiqSPOq7S1zFm31eBbWE74+dMlu+vXH9bxT6aqZqooHWW60UonncBTwNnuPuRsOst4Iywne2aan2tm4H/DoyE558Ehtz9wwz5OZHXsP/dcHytr2EWMAj8baju+p9mdhoNdi/cfQD4C+AQcITU57uXxrsfaeX6/GNhe2x6LXyDVMkEir+OXH9bRWmmYNAwzOzjwL3AKnd/L7rPU+G/bvsDm9lXgHfcfW+t81KiSaSK9re7exfwW1LVEifU+70ACHXqy0gFtzOB04AlNc1UmTTC55+PmV0PfAhsq3VemikYNMQ6y2bWSioQbHP3HSH5bTObHvZPB94J6dmuqZbXuhBYamavAXeTqir6S6DdzNKLKUXzcyKvYf/vAr+h9vfrMHDY3Z8Oz+8hFRwa6V4A/CHwqrsPunsS2EHqHjXa/Ugr1+c/ELbHpleNmX0N+ApweQhsUPx1/Ibs97IozRQMTqyzHFrbLwV21ThPo4TeDD8BXnT3H0R27QLSvSBWkGpLSKdfGXpSLADeDUXo3cAiM+sI/xkuCmkV5+5r3X2Gu88k9Rk/4e6XA08CF2e5hvS1XRyO95B+aejdMguYTarBryrc/S3gDTObE5L+HfACDXQvgkPAAjObEn6/0tfRUPcjoiyff9j3npktCJ/LlZHXqjgzW0KqKnWpux+P7Mr2OWf8/gr3Jtu9LE6lG4Dq6UGqx8E/k2qVv77W+cmQvy+SKvY+B/SFx4Wk6gUfB14G/jcwNRxvwA/D9fQD3ZHX+gZwMDy+XqPr+RIf9Sb6VPilPgj8HDg1pH8sPD8Y9n8qcv714doOUKGeHnny3wn0hvuxk1RvlIa7F8B64CXgeeB/keqpUvf3A/gZqXaOJKmS2lXl/PyB7vCZ/Ar4K8Z0FqjwdRwk1QaQ/jv/Ub7PmSzfX9nuZbEPTUchIiJNVU0kIiJZKBiIiIiCgYiIKBiIiAgKBiIigoKBiIigYCAiIsD/BzVo+l+0/9AfAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gmrAqjvUn2rv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "9275b71e-4b85-42ed-b260-8320fe823cea"
      },
      "source": [
        "m,c,r,p,se = stats.linregress(x=df['MD Feet'],y=df['Torue Feet-lbs'])\n",
        "x= df['MD Feet']\n",
        "y_modeled = pd.Series(map(lambda a : m*a + c,x ))\n",
        "plt.scatter(x='MD Feet',y='Torue Feet-lbs',data=df)\n",
        "plt.plot(x,y_modeled)\n",
        "tork_predictor = lambda a : m*a + c "
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mc_tliuTpGki",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2c5c0583-6e3d-4895-e93b-b4db52e7208a"
      },
      "source": [
        "print(r)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-0.9840888259274415\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V5SkW6JYp07l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# which tells us that it is a good correlation between measured depth and Torque. \n",
        "#now we have successfully built our own TORQUE PREDICTOR model, for the given data. \n",
        "#Example Prediction is given in the next cell-"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L_0NPnLpqOE7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "09ac59c8-baa4-435c-ce05-c0bc223a492f"
      },
      "source": [
        "tork_predictor(1000)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23937.732458430037"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ii9LWHGIqX_V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e1771658-9fb2-4dc5-e66c-c81661624dfd"
      },
      "source": [
        "# from the graph, it is clearly visible that the model fits perfectly for 10000-12000 MD\n",
        "tork_predictor(11000)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4562.463895763667"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r3VrSUsjq3rI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}